Water is a fundamental resource and, as such, the object of multiple environmental policies requiring systematic monitoring of its quality as a main management component. Automatic sensors, allowing for continuous monitoring of various water quality variables at high temporal resolution, offer new opportunities for enhancement of essential water quality data. This study investigates the potential of sensor-measured data to improve understanding and management of water quality at watershed level. Self-organizing data maps, non-linear canonical correlation analysis, and linear regressions are used to assess the relationships between multiple water quality and hydroclimatic variables for the case study of Lake Mälaren in Sweden, and its total catchment and various watersheds. The results indicate water discharge from dominant watersheds into a lake, and lake water temperature as possible proxies for some key water quality variables in the lake, such as blue-green algae; the latter is, in turn, identified as a potential good proxy for lake concentration of total nitrogen. The relationships between water discharges into the lake and lake water quality dynamics identify the dominant contributing watersheds for different water quality variables. Seasonality also plays an important role in determining some possible proxy relationships and their usefulness for different parts of the year.
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